Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations14446
Missing cells0
Missing cells (%)0.0%
Duplicate rows63
Duplicate rows (%)0.4%
Total size in memory1.7 MiB
Average record size in memory120.0 B

Variable types

DateTime2
Text4
Categorical3
Numeric6

Alerts

Dataset has 63 (0.4%) duplicate rowsDuplicates
lat is highly overall correlated with merch_lat and 1 other fieldsHigh correlation
long is highly overall correlated with merch_long and 1 other fieldsHigh correlation
merch_lat is highly overall correlated with lat and 1 other fieldsHigh correlation
merch_long is highly overall correlated with long and 1 other fieldsHigh correlation
state is highly overall correlated with lat and 3 other fieldsHigh correlation
is_fraud is highly imbalanced (72.3%) Imbalance

Reproduction

Analysis started2024-10-19 12:25:10.178243
Analysis finished2024-10-19 12:25:17.845765
Duration7.67 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Distinct12126
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Memory size113.0 KiB
Minimum2019-01-01 00:00:00
Maximum2020-12-31 23:59:00
2024-10-19T09:25:17.922877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:18.072542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct693
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size113.0 KiB
2024-10-19T09:25:18.294413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length39
Median length32
Mean length17.514952
Min length7

Characters and Unicode

Total characters253021
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"Stokes, Christiansen and Sipes"
2nd rowPredovic Inc
3rd rowWisozk and Sons
4th rowMurray-Smitham
5th rowFriesen Lt
ValueCountFrequency (%)
and 5319
 
15.8%
llc 1079
 
3.2%
inc 992
 
3.0%
sons 881
 
2.6%
lt 762
 
2.3%
plc 724
 
2.2%
group 505
 
1.5%
greenholt 149
 
0.4%
baumbach 141
 
0.4%
bahringer 131
 
0.4%
Other values (677) 22901
68.2%
2024-10-19T09:25:18.655214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 20557
 
8.1%
n 19719
 
7.8%
19138
 
7.6%
a 17620
 
7.0%
r 13741
 
5.4%
o 12513
 
4.9%
i 12237
 
4.8%
t 9843
 
3.9%
s 9131
 
3.6%
" 8876
 
3.5%
Other values (45) 109646
43.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 253021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 20557
 
8.1%
n 19719
 
7.8%
19138
 
7.6%
a 17620
 
7.0%
r 13741
 
5.4%
o 12513
 
4.9%
i 12237
 
4.8%
t 9843
 
3.9%
s 9131
 
3.6%
" 8876
 
3.5%
Other values (45) 109646
43.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 253021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 20557
 
8.1%
n 19719
 
7.8%
19138
 
7.6%
a 17620
 
7.0%
r 13741
 
5.4%
o 12513
 
4.9%
i 12237
 
4.8%
t 9843
 
3.9%
s 9131
 
3.6%
" 8876
 
3.5%
Other values (45) 109646
43.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 253021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 20557
 
8.1%
n 19719
 
7.8%
19138
 
7.6%
a 17620
 
7.0%
r 13741
 
5.4%
o 12513
 
4.9%
i 12237
 
4.8%
t 9843
 
3.9%
s 9131
 
3.6%
" 8876
 
3.5%
Other values (45) 109646
43.3%

category
Categorical

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size113.0 KiB
grocery_pos
1602 
gas_transport
1430 
shopping_net
1408 
shopping_pos
1354 
home
1304 
Other values (9)
7348 

Length

Max length14
Median length12
Mean length10.578707
Min length4

Characters and Unicode

Total characters152820
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgrocery_net
2nd rowshopping_net
3rd rowmisc_pos
4th rowgrocery_pos
5th rowhealth_fitness

Common Values

ValueCountFrequency (%)
grocery_pos 1602
11.1%
gas_transport 1430
9.9%
shopping_net 1408
9.7%
shopping_pos 1354
9.4%
home 1304
9.0%
kids_pets 1141
7.9%
personal_care 990
 
6.9%
entertainment 953
 
6.6%
health_fitness 891
 
6.2%
food_dining 870
 
6.0%
Other values (4) 2503
17.3%

Length

2024-10-19T09:25:18.783702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
grocery_pos 1602
11.1%
gas_transport 1430
9.9%
shopping_net 1408
9.7%
shopping_pos 1354
9.4%
home 1304
9.0%
kids_pets 1141
7.9%
personal_care 990
 
6.9%
entertainment 953
 
6.6%
health_fitness 891
 
6.2%
food_dining 870
 
6.0%
Other values (4) 2503
17.3%

Most occurring characters

ValueCountFrequency (%)
s 16099
10.5%
e 14230
9.3%
o 14081
9.2%
n 13375
8.8%
p 12864
8.4%
_ 11804
 
7.7%
t 11730
 
7.7%
r 10330
 
6.8%
i 9131
 
6.0%
g 7138
 
4.7%
Other values (10) 32038
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 152820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 16099
10.5%
e 14230
9.3%
o 14081
9.2%
n 13375
8.8%
p 12864
8.4%
_ 11804
 
7.7%
t 11730
 
7.7%
r 10330
 
6.8%
i 9131
 
6.0%
g 7138
 
4.7%
Other values (10) 32038
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 152820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 16099
10.5%
e 14230
9.3%
o 14081
9.2%
n 13375
8.8%
p 12864
8.4%
_ 11804
 
7.7%
t 11730
 
7.7%
r 10330
 
6.8%
i 9131
 
6.0%
g 7138
 
4.7%
Other values (10) 32038
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 152820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 16099
10.5%
e 14230
9.3%
o 14081
9.2%
n 13375
8.8%
p 12864
8.4%
_ 11804
 
7.7%
t 11730
 
7.7%
r 10330
 
6.8%
i 9131
 
6.0%
g 7138
 
4.7%
Other values (10) 32038
21.0%

amt
Real number (ℝ)

Distinct9266
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.43007
Minimum1
Maximum3261.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.0 KiB
2024-10-19T09:25:18.911025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.72
Q112.08
median51.52
Q3101.03
95-th percentile784.2175
Maximum3261.47
Range3260.47
Interquartile range (IQR)88.95

Descriptive statistics

Standard deviation231.35259
Coefficient of variation (CV)1.859298
Kurtosis16.110855
Mean124.43007
Median Absolute Deviation (MAD)41.82
Skewness3.4910004
Sum1797516.8
Variance53524.02
MonotonicityNot monotonic
2024-10-19T09:25:19.055585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.77 11
 
0.1%
1.64 11
 
0.1%
5.24 11
 
0.1%
4.39 10
 
0.1%
9.79 10
 
0.1%
2.72 10
 
0.1%
8.34 10
 
0.1%
6.06 9
 
0.1%
6.79 9
 
0.1%
1.21 9
 
0.1%
Other values (9256) 14346
99.3%
ValueCountFrequency (%)
1 1
 
< 0.1%
1.01 6
< 0.1%
1.02 2
 
< 0.1%
1.03 3
< 0.1%
1.04 6
< 0.1%
1.05 6
< 0.1%
1.06 3
< 0.1%
1.07 4
< 0.1%
1.08 1
 
< 0.1%
1.09 5
< 0.1%
ValueCountFrequency (%)
3261.47 1
< 0.1%
3178.51 1
< 0.1%
3154.76 1
< 0.1%
2612.14 1
< 0.1%
2416.72 1
< 0.1%
1782.53 1
< 0.1%
1566.58 1
< 0.1%
1555.17 1
< 0.1%
1526.91 1
< 0.1%
1484.88 1
< 0.1%

city
Text

Distinct176
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size113.0 KiB
2024-10-19T09:25:19.303980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length25
Median length18
Mean length8.3976187
Min length4

Characters and Unicode

Total characters121312
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWales
2nd rowWales
3rd rowWales
4th rowWales
5th rowWales
ValueCountFrequency (%)
city 565
 
3.1%
phoenix 297
 
1.6%
san 296
 
1.6%
springs 230
 
1.3%
river 214
 
1.2%
lake 213
 
1.2%
centerview 197
 
1.1%
orient 192
 
1.1%
mountain 191
 
1.0%
red 188
 
1.0%
Other values (195) 15652
85.8%
2024-10-19T09:25:19.668550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 11742
 
9.7%
a 11095
 
9.1%
n 9331
 
7.7%
o 9242
 
7.6%
l 7802
 
6.4%
r 7319
 
6.0%
i 7244
 
6.0%
t 6494
 
5.4%
s 4612
 
3.8%
d 3885
 
3.2%
Other values (40) 42546
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 11742
 
9.7%
a 11095
 
9.1%
n 9331
 
7.7%
o 9242
 
7.6%
l 7802
 
6.4%
r 7319
 
6.0%
i 7244
 
6.0%
t 6494
 
5.4%
s 4612
 
3.8%
d 3885
 
3.2%
Other values (40) 42546
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 11742
 
9.7%
a 11095
 
9.1%
n 9331
 
7.7%
o 9242
 
7.6%
l 7802
 
6.4%
r 7319
 
6.0%
i 7244
 
6.0%
t 6494
 
5.4%
s 4612
 
3.8%
d 3885
 
3.2%
Other values (40) 42546
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 11742
 
9.7%
a 11095
 
9.1%
n 9331
 
7.7%
o 9242
 
7.6%
l 7802
 
6.4%
r 7319
 
6.0%
i 7244
 
6.0%
t 6494
 
5.4%
s 4612
 
3.8%
d 3885
 
3.2%
Other values (40) 42546
35.1%

state
Categorical

High correlation 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size113.0 KiB
CA
3375 
MO
2329 
NE
1460 
OR
1211 
WA
1150 
Other values (8)
4921 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters28892
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAK
2nd rowAK
3rd rowAK
4th rowAK
5th rowAK

Common Values

ValueCountFrequency (%)
CA 3375
23.4%
MO 2329
16.1%
NE 1460
10.1%
OR 1211
 
8.4%
WA 1150
 
8.0%
WY 1100
 
7.6%
NM 1003
 
6.9%
CO 856
 
5.9%
AZ 673
 
4.7%
UT 597
 
4.1%
Other values (3) 692
 
4.8%

Length

2024-10-19T09:25:19.808789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 3375
23.4%
mo 2329
16.1%
ne 1460
10.1%
or 1211
 
8.4%
wa 1150
 
8.0%
wy 1100
 
7.6%
nm 1003
 
6.9%
co 856
 
5.9%
az 673
 
4.7%
ut 597
 
4.1%
Other values (3) 692
 
4.8%

Most occurring characters

ValueCountFrequency (%)
A 5371
18.6%
O 4396
15.2%
C 4231
14.6%
M 3332
11.5%
N 2463
8.5%
W 2250
7.8%
E 1460
 
5.1%
R 1211
 
4.2%
Y 1100
 
3.8%
Z 673
 
2.3%
Other values (6) 2405
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5371
18.6%
O 4396
15.2%
C 4231
14.6%
M 3332
11.5%
N 2463
8.5%
W 2250
7.8%
E 1460
 
5.1%
R 1211
 
4.2%
Y 1100
 
3.8%
Z 673
 
2.3%
Other values (6) 2405
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5371
18.6%
O 4396
15.2%
C 4231
14.6%
M 3332
11.5%
N 2463
8.5%
W 2250
7.8%
E 1460
 
5.1%
R 1211
 
4.2%
Y 1100
 
3.8%
Z 673
 
2.3%
Other values (6) 2405
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5371
18.6%
O 4396
15.2%
C 4231
14.6%
M 3332
11.5%
N 2463
8.5%
W 2250
7.8%
E 1460
 
5.1%
R 1211
 
4.2%
Y 1100
 
3.8%
Z 673
 
2.3%
Other values (6) 2405
8.3%

lat
Real number (ℝ)

High correlation 

Distinct183
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.787692
Minimum20.0271
Maximum66.6933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.0 KiB
2024-10-19T09:25:19.938241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20.0271
5-th percentile33.3305
Q136.7154
median39.6662
Q341.9404
95-th percentile47.4974
Maximum66.6933
Range46.6662
Interquartile range (IQR)5.225

Descriptive statistics

Standard deviation5.3170389
Coefficient of variation (CV)0.13363527
Kurtosis5.8560156
Mean39.787692
Median Absolute Deviation (MAD)2.9011
Skewness0.69130654
Sum574773
Variance28.270903
MonotonicityNot monotonic
2024-10-19T09:25:20.089130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.7897 197
 
1.4%
48.8878 192
 
1.3%
33.5623 190
 
1.3%
43.0048 187
 
1.3%
41.1558 187
 
1.3%
33.2887 183
 
1.3%
38.9999 178
 
1.2%
34.3795 169
 
1.2%
43.6498 165
 
1.1%
48.34 163
 
1.1%
Other values (173) 12635
87.5%
ValueCountFrequency (%)
20.0271 109
0.8%
20.0827 63
 
0.4%
32.274 35
 
0.2%
32.7185 41
 
0.3%
32.9396 126
0.9%
33.0067 107
0.7%
33.2887 183
1.3%
33.3305 105
0.7%
33.4317 9
 
0.1%
33.5494 71
 
0.5%
ValueCountFrequency (%)
66.6933 12
 
0.1%
65.6899 36
 
0.2%
64.7556 111
0.8%
55.4732 14
 
0.1%
48.8878 192
1.3%
48.4786 119
0.8%
48.34 163
1.1%
48.0379 15
 
0.1%
47.9657 40
 
0.3%
47.6633 10
 
0.1%

long
Real number (ℝ)

High correlation 

Distinct183
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-110.87422
Minimum-165.6723
Maximum-89.6287
Zeros0
Zeros (%)0.0%
Negative14446
Negative (%)100.0%
Memory size113.0 KiB
2024-10-19T09:25:20.234441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-165.6723
5-th percentile-123.9743
Q1-120.4158
median-111.0985
Q3-101.136
95-th percentile-91.8912
Maximum-89.6287
Range76.0436
Interquartile range (IQR)19.2798

Descriptive statistics

Standard deviation12.985813
Coefficient of variation (CV)-0.11712202
Kurtosis2.4767939
Mean-110.87422
Median Absolute Deviation (MAD)9.5664
Skewness-0.83923821
Sum-1601689.1
Variance168.63133
MonotonicityNot monotonic
2024-10-19T09:25:20.491554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-93.8702 197
 
1.4%
-118.2105 192
 
1.3%
-112.0559 190
 
1.3%
-108.8964 187
 
1.3%
-101.136 187
 
1.3%
-111.0985 183
 
1.3%
-109.615 178
 
1.2%
-118.523 169
 
1.2%
-116.4306 165
 
1.1%
-122.3456 163
 
1.1%
Other values (173) 12635
87.5%
ValueCountFrequency (%)
-165.6723 111
0.8%
-156.292 36
 
0.2%
-155.488 63
0.4%
-155.3697 109
0.8%
-153.994 12
 
0.1%
-133.1171 14
 
0.1%
-124.4409 64
0.4%
-124.2174 93
0.6%
-124.1587 59
0.4%
-124.1437 95
0.7%
ValueCountFrequency (%)
-89.6287 90
0.6%
-90.2848 8
 
0.1%
-90.2907 36
 
0.2%
-90.387 126
0.9%
-90.4504 45
 
0.3%
-90.5255 73
0.5%
-90.9362 24
 
0.2%
-91.0243 156
1.1%
-91.0664 33
 
0.2%
-91.4867 49
 
0.3%

city_pop
Real number (ℝ)

Distinct174
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106537
Minimum46
Maximum2383912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.0 KiB
2024-10-19T09:25:20.650083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile85
Q1493
median1645
Q335439
95-th percentile841711
Maximum2383912
Range2383866
Interquartile range (IQR)34946

Descriptive statistics

Standard deviation290291.61
Coefficient of variation (CV)2.7247961
Kurtosis14.772032
Mean106537
Median Absolute Deviation (MAD)1500
Skewness3.6583766
Sum1.5390335 × 109
Variance8.4269218 × 1010
MonotonicityNot monotonic
2024-10-19T09:25:20.810279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1312922 297
 
2.1%
241 282
 
2.0%
2368 197
 
1.4%
149 192
 
1.3%
1789 187
 
1.3%
1645 187
 
1.3%
2872 183
 
1.3%
46 178
 
1.2%
34882 169
 
1.2%
84106 165
 
1.1%
Other values (164) 12409
85.9%
ValueCountFrequency (%)
46 178
1.2%
49 72
0.5%
60 72
0.5%
61 124
0.9%
73 126
0.9%
85 163
1.1%
100 82
0.6%
104 34
 
0.2%
110 59
 
0.4%
121 19
 
0.1%
ValueCountFrequency (%)
2383912 29
 
0.2%
1312922 297
2.1%
1241364 148
1.0%
973849 148
1.0%
927396 45
 
0.3%
841711 73
 
0.5%
837792 19
 
0.1%
757530 109
 
0.8%
641349 81
 
0.6%
545147 120
0.8%

job
Text

Distinct163
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size113.0 KiB
2024-10-19T09:25:21.016497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length49
Median length33
Mean length21.130901
Min length6

Characters and Unicode

Total characters305257
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"Administrator, education"
2nd row"Administrator, education"
3rd row"Administrator, education"
4th row"Administrator, education"
5th row"Administrator, education"
ValueCountFrequency (%)
engineer 1699
 
5.2%
officer 1196
 
3.6%
surveyor 964
 
2.9%
manager 804
 
2.5%
scientist 726
 
2.2%
education 694
 
2.1%
research 542
 
1.7%
therapist 527
 
1.6%
analyst 513
 
1.6%
land/geomatics 465
 
1.4%
Other values (209) 24638
75.2%
2024-10-19T09:25:21.363748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 32382
 
10.6%
i 25292
 
8.3%
r 22629
 
7.4%
t 21181
 
6.9%
n 21083
 
6.9%
a 20227
 
6.6%
18322
 
6.0%
o 17270
 
5.7%
c 16197
 
5.3%
s 15966
 
5.2%
Other values (41) 94708
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 305257
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 32382
 
10.6%
i 25292
 
8.3%
r 22629
 
7.4%
t 21181
 
6.9%
n 21083
 
6.9%
a 20227
 
6.6%
18322
 
6.0%
o 17270
 
5.7%
c 16197
 
5.3%
s 15966
 
5.2%
Other values (41) 94708
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 305257
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 32382
 
10.6%
i 25292
 
8.3%
r 22629
 
7.4%
t 21181
 
6.9%
n 21083
 
6.9%
a 20227
 
6.6%
18322
 
6.0%
o 17270
 
5.7%
c 16197
 
5.3%
s 15966
 
5.2%
Other values (41) 94708
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 305257
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 32382
 
10.6%
i 25292
 
8.3%
r 22629
 
7.4%
t 21181
 
6.9%
n 21083
 
6.9%
a 20227
 
6.6%
18322
 
6.0%
o 17270
 
5.7%
c 16197
 
5.3%
s 15966
 
5.2%
Other values (41) 94708
31.0%

dob
Date

Distinct187
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size113.0 KiB
Minimum1927-09-09 00:00:00
Maximum2001-07-26 00:00:00
2024-10-19T09:25:21.511686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:21.668161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct14383
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size113.0 KiB
2024-10-19T09:25:21.861519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters462272
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14320 ?
Unique (%)99.1%

Sample

1st rowa3806e984cec6ac0096d8184c64ad3a1
2nd rowa59185fe1b9ccf21323f581d7477573f
3rd row86ba3a888b42cd3925881fa34177b4e0
4th row3a068fe1d856f0ecedbed33e4b5f4496
5th row891cdd1191028759dc20dc224347a0ff
ValueCountFrequency (%)
f1edc60904bafa8aac00a0f5e9026d0c 2
 
< 0.1%
9bc5cb494abc3af2b02ca33e0d076f74 2
 
< 0.1%
7d7d61dc3b301c78ca3c0cf73e8ed72e 2
 
< 0.1%
049087fe5d27b77c7238fa46bb18c99d 2
 
< 0.1%
fdc202f9f1dd556a51775c6d8060c58d 2
 
< 0.1%
dec7f564c518a3f5878016461d766ffa 2
 
< 0.1%
4d7e567247b6c4529ce4c32c03b2f040 2
 
< 0.1%
b87c92d4824758e704da572891697fed 2
 
< 0.1%
98649f992d93377c62c09e51a1bd2cc9 2
 
< 0.1%
5fbe827807ec9f557f6242bb48db0e51 2
 
< 0.1%
Other values (14373) 14426
99.9%
2024-10-19T09:25:22.185212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
b 29238
 
6.3%
5 29118
 
6.3%
8 29057
 
6.3%
d 29019
 
6.3%
6 28981
 
6.3%
e 28914
 
6.3%
9 28900
 
6.3%
4 28900
 
6.3%
c 28887
 
6.2%
a 28868
 
6.2%
Other values (6) 172390
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 462272
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 29238
 
6.3%
5 29118
 
6.3%
8 29057
 
6.3%
d 29019
 
6.3%
6 28981
 
6.3%
e 28914
 
6.3%
9 28900
 
6.3%
4 28900
 
6.3%
c 28887
 
6.2%
a 28868
 
6.2%
Other values (6) 172390
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 462272
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 29238
 
6.3%
5 29118
 
6.3%
8 29057
 
6.3%
d 29019
 
6.3%
6 28981
 
6.3%
e 28914
 
6.3%
9 28900
 
6.3%
4 28900
 
6.3%
c 28887
 
6.2%
a 28868
 
6.2%
Other values (6) 172390
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 462272
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 29238
 
6.3%
5 29118
 
6.3%
8 29057
 
6.3%
d 29019
 
6.3%
6 28981
 
6.3%
e 28914
 
6.3%
9 28900
 
6.3%
4 28900
 
6.3%
c 28887
 
6.2%
a 28868
 
6.2%
Other values (6) 172390
37.3%

merch_lat
Real number (ℝ)

High correlation 

Distinct14376
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.787991
Minimum19.032689
Maximum67.510267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.0 KiB
2024-10-19T09:25:22.327285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum19.032689
5-th percentile33.086286
Q136.794655
median39.620953
Q342.27574
95-th percentile47.832952
Maximum67.510267
Range48.477578
Interquartile range (IQR)5.4810852

Descriptive statistics

Standard deviation5.3605934
Coefficient of variation (CV)0.13472893
Kurtosis5.748108
Mean39.787991
Median Absolute Deviation (MAD)2.7316305
Skewness0.68076238
Sum574777.32
Variance28.735962
MonotonicityNot monotonic
2024-10-19T09:25:22.476244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.998205 2
 
< 0.1%
39.164469 2
 
< 0.1%
38.211376 2
 
< 0.1%
38.748484 2
 
< 0.1%
38.376171 2
 
< 0.1%
38.893999 2
 
< 0.1%
38.435536 2
 
< 0.1%
39.42207 2
 
< 0.1%
39.157345 2
 
< 0.1%
40.701366 2
 
< 0.1%
Other values (14366) 14426
99.9%
ValueCountFrequency (%)
19.032689 1
< 0.1%
19.040141 1
< 0.1%
19.04251 1
< 0.1%
19.070393 1
< 0.1%
19.101256 1
< 0.1%
19.140535 1
< 0.1%
19.161782 1
< 0.1%
19.165823 1
< 0.1%
19.167279 1
< 0.1%
19.169435 1
< 0.1%
ValueCountFrequency (%)
67.510267 1
< 0.1%
67.441518 1
< 0.1%
67.397018 1
< 0.1%
67.188111 1
< 0.1%
67.064277 1
< 0.1%
66.835174 1
< 0.1%
66.659242 1
< 0.1%
66.650388 1
< 0.1%
66.646051 1
< 0.1%
66.645176 1
< 0.1%

merch_long
Real number (ℝ)

High correlation 

Distinct14380
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-110.87489
Minimum-166.67068
Maximum-88.646366
Zeros0
Zeros (%)0.0%
Negative14446
Negative (%)100.0%
Memory size113.0 KiB
2024-10-19T09:25:22.619835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-166.67068
5-th percentile-123.78017
Q1-120.14625
median-111.19263
Q3-100.44682
95-th percentile-91.975339
Maximum-88.646366
Range78.024319
Interquartile range (IQR)19.699431

Descriptive statistics

Standard deviation12.995596
Coefficient of variation (CV)-0.11720955
Kurtosis2.4706392
Mean-110.87489
Median Absolute Deviation (MAD)9.388988
Skewness-0.83695013
Sum-1601698.7
Variance168.88552
MonotonicityNot monotonic
2024-10-19T09:25:22.778533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-109.986757 2
 
< 0.1%
-110.32181 2
 
< 0.1%
-110.36249 2
 
< 0.1%
-109.677707 2
 
< 0.1%
-109.844716 2
 
< 0.1%
-109.555496 2
 
< 0.1%
-109.126592 2
 
< 0.1%
-109.044284 2
 
< 0.1%
-99.38238 2
 
< 0.1%
-121.383937 2
 
< 0.1%
Other values (14370) 14426
99.9%
ValueCountFrequency (%)
-166.670685 1
< 0.1%
-166.629875 1
< 0.1%
-166.625519 1
< 0.1%
-166.596324 1
< 0.1%
-166.573982 1
< 0.1%
-166.550779 2
< 0.1%
-166.539712 1
< 0.1%
-166.522522 1
< 0.1%
-166.414244 1
< 0.1%
-166.410533 1
< 0.1%
ValueCountFrequency (%)
-88.646366 1
< 0.1%
-88.651755 1
< 0.1%
-88.720514 1
< 0.1%
-88.742942 1
< 0.1%
-88.795852 1
< 0.1%
-88.825507 1
< 0.1%
-88.825894 1
< 0.1%
-88.872009 1
< 0.1%
-88.901988 1
< 0.1%
-88.927438 1
< 0.1%

is_fraud
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.0 KiB
0
12600 
1
1844 
1"2020-12-24 16:56:24"
 
1
0"2019-01-01 00:00:44"
 
1

Length

Max length22
Median length1
Mean length1.0029074
Min length1

Characters and Unicode

Total characters14488
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 12600
87.2%
1 1844
 
12.8%
1"2020-12-24 16:56:24" 1
 
< 0.1%
0"2019-01-01 00:00:44" 1
 
< 0.1%

Length

2024-10-19T09:25:22.926303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-19T09:25:23.037524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12600
87.2%
1 1844
 
12.8%
1"2020-12-24 1
 
< 0.1%
16:56:24 1
 
< 0.1%
0"2019-01-01 1
 
< 0.1%
00:00:44 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 12610
87.0%
1 1850
 
12.8%
2 6
 
< 0.1%
" 4
 
< 0.1%
- 4
 
< 0.1%
4 4
 
< 0.1%
: 4
 
< 0.1%
2
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12610
87.0%
1 1850
 
12.8%
2 6
 
< 0.1%
" 4
 
< 0.1%
- 4
 
< 0.1%
4 4
 
< 0.1%
: 4
 
< 0.1%
2
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12610
87.0%
1 1850
 
12.8%
2 6
 
< 0.1%
" 4
 
< 0.1%
- 4
 
< 0.1%
4 4
 
< 0.1%
: 4
 
< 0.1%
2
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12610
87.0%
1 1850
 
12.8%
2 6
 
< 0.1%
" 4
 
< 0.1%
- 4
 
< 0.1%
4 4
 
< 0.1%
: 4
 
< 0.1%
2
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%

Interactions

2024-10-19T09:25:16.831103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:13.788885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:14.386399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:14.958437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:15.571525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:16.258560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:16.937779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:13.888098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:14.483014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:15.060935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:15.670526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:16.354759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:17.035610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:13.979586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:14.570099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:15.156677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:15.762714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:16.443178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:17.145637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:14.085457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:14.672025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:15.261875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:15.866304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:16.544214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:17.248899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:14.183189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:14.765201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:15.363784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:15.962349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:16.637620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:17.348685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:14.277839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:14.852497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:15.459596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:16.148429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-19T09:25:16.726093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-19T09:25:23.123292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
amtcategorycity_popis_fraudlatlongmerch_latmerch_longstate
amt1.0000.1960.0240.3810.021-0.0050.020-0.0070.029
category0.1961.0000.0090.1650.0240.0210.0190.0210.020
city_pop0.0240.0091.0000.028-0.338-0.078-0.340-0.0820.297
is_fraud0.3810.1650.0281.0000.0620.0580.0580.0550.060
lat0.0210.024-0.3380.0621.000-0.1790.987-0.1790.745
long-0.0050.021-0.0780.058-0.1791.000-0.1760.9950.750
merch_lat0.0200.019-0.3400.0580.987-0.1761.000-0.1760.713
merch_long-0.0070.021-0.0820.055-0.1790.995-0.1761.0000.771
state0.0290.0200.2970.0600.7450.7500.7130.7711.000

Missing values

2024-10-19T09:25:17.515114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-19T09:25:17.748665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

trans_date_trans_timemerchantcategoryamtcitystatelatlongcity_popjobdobtrans_nummerch_latmerch_longis_fraud
004-01-2019 00:58"Stokes, Christiansen and Sipes"grocery_net14.37WalesAK64.7556-165.6723145"Administrator, education"09-11-1939a3806e984cec6ac0096d8184c64ad3a165.654142-164.7226031
104-01-2019 15:06Predovic Incshopping_net966.11WalesAK64.7556-165.6723145"Administrator, education"09-11-1939a59185fe1b9ccf21323f581d7477573f65.468863-165.4731271
204-01-2019 22:37Wisozk and Sonsmisc_pos49.61WalesAK64.7556-165.6723145"Administrator, education"09-11-193986ba3a888b42cd3925881fa34177b4e065.347667-165.9145421
304-01-2019 23:06Murray-Smithamgrocery_pos295.26WalesAK64.7556-165.6723145"Administrator, education"09-11-19393a068fe1d856f0ecedbed33e4b5f449664.445035-166.0802071
404-01-2019 23:59Friesen Lthealth_fitness18.17WalesAK64.7556-165.6723145"Administrator, education"09-11-1939891cdd1191028759dc20dc224347a0ff65.447094-165.4468431
505-01-2019 03:15"Raynor, Reinger and Hagenes"gas_transport20.45WalesAK64.7556-165.6723145"Administrator, education"09-11-1939ef010a5f4f570d306a050a368ee2729d64.088838-165.1040781
605-01-2019 03:21Heller-Langoshgas_transport18.19WalesAK64.7556-165.6723145"Administrator, education"09-11-19398e2d2fae5319d31c887dddbc70627ac463.917785-165.8276211
705-01-2019 11:31Padberg-Welchgrocery_pos367.29BrowningMO40.0290-93.1607602Cytogeneticist14-07-19545fbe827807ec9f557f6242bb48db0e5139.167065-93.7052451
805-01-2019 18:03McGlynn-Heathcotemisc_net768.15WalesAK64.7556-165.6723145"Administrator, education"09-11-1939fba83e0a3adb530251295ab72a96b71964.623325-166.4039731
905-01-2019 22:02Dooley-Thompsonmisc_net849.49WalesAK64.7556-165.6723145"Administrator, education"09-11-1939b87c92d4824758e704da572891697fed65.266065-164.8653521
trans_date_trans_timemerchantcategoryamtcitystatelatlongcity_popjobdobtrans_nummerch_latmerch_longis_fraud
1443622-01-2019 00:18"Connelly, Reichert and Fritsch"gas_transport93.23UnionvilleMO40.4815-92.99513805"Investment banker, corporate"15-09-195058d980b2db4f0581aaa3e62967072efa40.527285-93.8596740
1443722-01-2019 00:19"Kuhic, Bins and Pfeffe"shopping_net4.65EugeneOR44.0385-123.0614191096"Scientist, physiological"06-04-19646f552aa7397e6e1c012c25ecfc0cc9b743.821635-122.4972360
1443822-01-2019 00:23Sporer Incgas_transport51.57CarlottaCA40.5070-123.97431139"Therapist, occupational"15-01-1951c10ca0af6656b71e6da577da9db6c8c340.556556-124.8876580
1443922-01-2019 00:32"Willms, Kris and Bergnaum"shopping_pos145.60AthenaOR45.8289-118.49711302Dealer18-10-1976c206b545e52a142e1009fb0bd3e3f2ac46.592719-118.0022890
1444022-01-2019 00:37Wiza LLCmisc_pos37.92SyracuseMO38.6547-92.8929628"Radiographer, diagnostic"18-12-1961a98a9e2ca6a7c605c34a4298be3ad60639.245730-92.4413880
1444122-01-2019 00:37Hudson-Gradyshopping_pos122.00AthenaOR45.8289-118.49711302Dealer18-10-1976699a4c06b22711bf3e0d8ef91232d35646.442439-118.5242140
1444222-01-2019 00:41"Nienow, Ankunding and Collie"misc_pos9.07GardinerOR43.7857-124.1437260"Engineer, maintenance"01-09-1956080d620d24815c7d6c637cf0b71dde8e42.901265-124.9953170
1444322-01-2019 00:42Pacocha-O'Reillygrocery_pos104.84AlvaWY44.6873-104.4414110"Administrator, local government"16-05-19733c346c8cd627c5fe3ed57430db2e9ae745.538062-104.5421170
1444422-01-2019 00:48"Bins, Balistreri and Beatty"shopping_pos268.16WalesAK64.7556-165.6723145"Administrator, education"09-11-1939e66ffcc95ba7fc490486242af1205d0464.081462-165.8986980
1444522-01-2019 00:55Daugherty-Thompsonfood_dining50.09UnionvilleMO40.4815-92.99513805"Investment banker, corporate"15-09-195065e7370f473f9b9d75796c8033a7c92940.387243-92.2248710

Duplicate rows

Most frequently occurring

trans_date_trans_timemerchantcategoryamtcitystatelatlongcity_popjobdobtrans_nummerch_latmerch_longis_fraud# duplicates
004-01-2019 00:58"Stokes, Christiansen and Sipes"grocery_net14.37WalesAK64.7556-165.6723145"Administrator, education"09-11-1939a3806e984cec6ac0096d8184c64ad3a165.654142-164.72260312
104-01-2019 15:06Predovic Incshopping_net966.11WalesAK64.7556-165.6723145"Administrator, education"09-11-1939a59185fe1b9ccf21323f581d7477573f65.468863-165.47312712
204-01-2019 22:37Wisozk and Sonsmisc_pos49.61WalesAK64.7556-165.6723145"Administrator, education"09-11-193986ba3a888b42cd3925881fa34177b4e065.347667-165.91454212
304-01-2019 23:06Murray-Smithamgrocery_pos295.26WalesAK64.7556-165.6723145"Administrator, education"09-11-19393a068fe1d856f0ecedbed33e4b5f449664.445035-166.08020712
404-01-2019 23:59Friesen Lthealth_fitness18.17WalesAK64.7556-165.6723145"Administrator, education"09-11-1939891cdd1191028759dc20dc224347a0ff65.447094-165.44684312
505-01-2019 03:15"Raynor, Reinger and Hagenes"gas_transport20.45WalesAK64.7556-165.6723145"Administrator, education"09-11-1939ef010a5f4f570d306a050a368ee2729d64.088838-165.10407812
605-01-2019 03:21Heller-Langoshgas_transport18.19WalesAK64.7556-165.6723145"Administrator, education"09-11-19398e2d2fae5319d31c887dddbc70627ac463.917785-165.82762112
705-01-2019 11:31Padberg-Welchgrocery_pos367.29BrowningMO40.0290-93.1607602Cytogeneticist14-07-19545fbe827807ec9f557f6242bb48db0e5139.167065-93.70524512
805-01-2019 18:03McGlynn-Heathcotemisc_net768.15WalesAK64.7556-165.6723145"Administrator, education"09-11-1939fba83e0a3adb530251295ab72a96b71964.623325-166.40397312
905-01-2019 22:02Dooley-Thompsonmisc_net849.49WalesAK64.7556-165.6723145"Administrator, education"09-11-1939b87c92d4824758e704da572891697fed65.266065-164.86535212